A Method of Re-Baselining a Plurality of AI Models and a Control System thereof

Abstract

A method is for re-baselining a plurality of AI models residing in a plurality of independent edge devices. The AI models self-learn in the edge devices and are extracted from the edge devices to a version-controlled database. This is followed by diagnoses of the learnings of the self-learnt AI models on a digital twin environment of the edge device. A group of self-learnt AI models with good learnings are selected based on the diagnosis. Next these groups of selected AI models are subjected to federated learning to get a re-baselined model. The re-baselined model is validated using the digital twin and is pushed into the plurality of edge devices using firmware over the air.

1 . A method of re-baselining a plurality of AI models residing in a plurality of independent edge devices, the AI models adapted to self-learn in the edge devices, the method comprising:
 extracting the self-learned AI models from the edge devices to a version controlled database;   diagnosing learnings of the extracted self-learned AI models on a digital twin environment of the corresponding edge device;   selecting a group of self-learned AI models based on said diagnosis;   performing federated learning on the selected group of AI models to get a re-baselined model;   validating the re-baselined model on said digital twin environment; and   pushing the re-baselined model into the plurality of edge devices using firmware over the air.   
     
     
         2 . The method of  claim 1 , wherein each edge device of the plurality of edge devices run a specified version of the AI model. 
     
     
         3 . The method of  claim 1 , wherein the diagnosis of learnings is done by testing a performance of the extracted self-learned AI models on critical tasks. 
     
     
         4 . The method of  claim 1 , wherein the selection of the group of AI models is based on a performance of the AI models in critical tasks. 
     
     
         5 . A control system for re-baselining a plurality of AI models, the control system comprising:
 a processor;   a memory; and   at least one network interface,   wherein the plurality of AI models reside in a plurality of independent edge devices,   wherein the AI models are adapted to self-learn in the plurality of independent edge devices,   wherein the plurality of independent edge devices are connected to the at least one network interface,   wherein the at least one network interface is configured to extract the self-learned AI models from the edge devices to a version controlled database stored in the memory;   wherein the processor is configured to:
 diagnose the learnings of the extracted self-learned AI models on a digital twin environment of the corresponding edge device; 
 select a group of self-learned AI models based on said diagnosis; 
 perform federated learning on the selected group of AI models to get a re-baselined model; 
 validate the re-baselined model on said digital twin environment; and 
 push the re-baselined model into the plurality of edge devices through the network interface. 
   
     
     
         6 . The control system as claimed in  claim 5 , wherein each edge device of the plurality of edge devices run a specified version of the AI model. 
     
     
         7 . The control system as claimed in  claim 5 , wherein the diagnosis of learnings is done by testing a performance of the extracted self-learned AI models on critical tasks. 
     
     
         8 . The control system as claimed in  claim 5 , wherein the selection of the group of AI models is based on a performance of the AI models in critical tasks.